French-ify several plotting functions

R/extra.R

@@ -1,49 +1,68 @@
-plot_cpue_predictions <- function(dat, model_version = "Combined", scale = FALSE) {
+plot_cpue_predictions <- function(dat,
+                                  model_version = "Combined",
+                                  scale = FALSE,
+                                  french = FALSE) {
   if (scale) {
-    dat <- dat %>%
-      group_by(.data$formula_version, .data$model, .data$area) %>%
-      mutate(geo_mean = exp(mean(log(.data$est)))) %>%
-      mutate(upr = .data$upr / .data$geo_mean, lwr = .data$lwr / .data$geo_mean,
-        est = .data$est / .data$geo_mean) %>%
+    dat <- dat |>
+      group_by(.data$formula_version, .data$model, .data$area) |>
+      mutate(geo_mean = exp(mean(log(.data$est)))) |>
+      mutate(upr = .data$upr / .data$geo_mean,
+             lwr = .data$lwr / .data$geo_mean,
+             est = .data$est / .data$geo_mean) |>
       ungroup()
   }
 
-  unstandardized <- dat %>%
-    filter(.data$formula_version == "Unstandardized") %>%
+  unstandardized <- dat |>
+    filter(.data$formula_version == "Unstandardized") |>
     rename(est_unstandardized = .data$est,
-      lwr_unstandardized = .data$lwr, upr_unstandardized = .data$upr) %>%
+           lwr_unstandardized = .data$lwr, upr_unstandardized = .data$upr) |>
     select(.data$year, .data$area, .data$model,
-      .data$est_unstandardized, .data$lwr_unstandardized,
-      .data$upr_unstandardized)
+           .data$est_unstandardized, .data$lwr_unstandardized,
+           .data$upr_unstandardized)
 
-  temp <- dat %>%
-    filter(.data$formula_version != "Unstandardized") %>%
-    left_join(unstandardized, by = c("area", "year", "model")) %>%
-    filter(model == model_version) %>%
-    mutate(formula_version = gsub("\\+ ", " ", .data$formula_version)) %>%
-    mutate(
-      formula_version =
-        gsub("Full standardization", "All variables", .data$formula_version)
-    ) %>%
-    mutate(
-      formula_version =
-        forcats::fct_relevel(.data$formula_version, "All variables", after = Inf)
+  temp <- dat |>
+    filter(.data$formula_version != "Unstandardized") |>
+    left_join(unstandardized, by = c("area", "year", "model")) |>
+    filter(model == model_version) |>
+    mutate(formula_version = gsub("\\+ ", " ", .data$formula_version)) |>
+    mutate(formula_version =
+             gsub("Full standardization",
+                  ifelse(french,
+                         "Toutes les variables",
+                         "All variables"),
+                  .data$formula_version)
+    ) |>
+    mutate(formula_version =
+             forcats::fct_relevel(.data$formula_version,
+                                  ifelse(french,
+                                         "Toutes les variables",
+                                         "All variables"),
+                                  after = Inf)
     )
-  temp %>%
+
+  if(!scale){
+    y_label <- ifelse(french,
+                      "CPUE (kg/heure)",
+                      "CPUE (kg/hour)")
+  }else{
+    y_label <- ifelse(french,
+                      "CPUE (kg/h) moyenne géométrique divisée",
+                      "CPUE (kg/hour) divided\nby geometric mean")
+  }
+
+  temp |>
     ggplot(aes_string("year", "est", ymin = "upr", ymax = "lwr")) +
-    geom_line(aes_string(y = "est_unstandardized"), colour = "grey30", lty = 1) +
-    ggplot2::geom_ribbon(
-      aes_string(ymin = "lwr_unstandardized", ymax = "upr_unstandardized"),
-      fill = "#00000030"
-    ) +
+    geom_line(aes_string(y = "est_unstandardized"),
+              colour = "grey30",
+              lty = 1) +
+    ggplot2::geom_ribbon(aes_string(ymin = "lwr_unstandardized",
+                                    ymax = "upr_unstandardized"),
+                         fill = "#00000030") +
     ggplot2::geom_ribbon(alpha = 0.4, fill = "red") +
     geom_line(colour = "red") +
     theme_pbs() +
     facet_grid(area ~ formula_version, scales = "free_y") +
-    labs(
-      y = if (!scale) "CPUE (kg/hour)" else "CPUE (kg/hour) divided\nby geometric mean",
-      x = ""
-    ) +
+    labs(y = y_label, x = "") +
     ylim(0, NA) +
     guides(colour = "none", fill = "none")
 }

R/plot_predictor_bubbles.R

@@ -4,6 +4,7 @@
 #' @param variable The column to summarize (character value)
 #' @param group_id The events to count over (e.g. `"fishing_event_id"` or `"trip_id"`)
 #' @param ncol Number of facetted columns (facets by area)
+#' @param french If `TRUE`, all text will appear as French in the plot
 #'
 #' @return A ggplot object
 #' @export
@@ -16,7 +17,7 @@
 #'   area = rep("a", 400))
 #' plot_predictor_bubbles(d, "my_predictor")
 plot_predictor_bubbles <- function(dat, variable,
-  group_id = "fishing_event_id", ncol = 2) {
+  group_id = "fishing_event_id", ncol = 2, french = FALSE) {
   temp_pos <- dat %>%
     filter(spp_catch > 0) %>%
     group_by(area, year, !!rlang::sym(variable)) %>%
@@ -32,15 +33,19 @@ plot_predictor_bubbles <- function(dat, variable,
     mutate(n_tot = sum(n)) %>%
     ungroup()
 
+  leg_title <- ifelse(french,
+                      paste0("Nombre de\n", group_id),
+                      paste0("Number of\n", group_id))
+
   p <- temp_pos %>%
     ggplot(aes_string("as.factor(year)", y = variable)) +
     geom_point(aes_string(size = "n", fill = "n"), alpha = 0.4, pch = 21) +
     geom_point(data = temp_all, aes_string(size = "n"), alpha = 0.4, pch = 21) +
     facet_wrap(~area, scales = "free", ncol = ncol) +
     ggplot2::scale_x_discrete(breaks = seq(1950, 2020, 5)) +
-    xlab("") + ylab(firstup(gsub("_", " ", variable))) +
-    labs(size = paste0("Number of\n", group_id)) +
-    labs(fill = paste0("Number of\n", group_id)) +
+    xlab("") + ylab(firstup(gsub("_", " ", en2fr(variable, translate = french)))) +
+    labs(size = leg_title) +
+    labs(fill = leg_title) +
     ggplot2::scale_size_continuous(range = c(0, 7), breaks = c(1, 10, 100, 500, 1000)) +
     ggplot2::scale_fill_viridis_c(trans = "log", breaks = c(1, 10, 100, 500, 1000)) +
     guides(fill = guide_legend(reverse=T), size = guide_legend(reverse=T)) +